{"id":2260426,"url":"https://alion.io/job/astrazeneca-associate-director-clinical-ai-evaluation-and-responsible-deployment","title":"Associate Director, Clinical AI Evaluation and Responsible Deployment","company":{"id":13195,"name":"AstraZeneca","domain":"astrazeneca.com","url":"https://alion.io/company/astra-zeneca","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"A","score":93,"open_postings":165,"ghost_share":0.006,"stale_share":0.388,"repost_share":0.103,"time_to_fill_p50_days":19,"computed_at":"2026-11-05T05:54:30Z"}},"role":"Leadership","role_family":"Leadership","seniority":"head","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Barcelona, Spain"],"countries":["ES"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":86000,"max_usd":168000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":9},"experience_years_min":4,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"Function Calling","optional":false},{"name":"GCP","optional":false},{"name":"Hallucination","optional":false},{"name":"Human-in-the-Loop","optional":false},{"name":"LLM Evaluation","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"SQL","optional":false},{"name":"Structured Outputs","optional":false},{"name":"LLM","optional":true},{"name":"LLMOps","optional":true}],"status":"live","first_seen_at":"2026-10-08T00:00:00Z","employer_posted_date":"2026-10-29","last_verified_at":"2026-11-05T21:57:24Z","board_verified":true,"closed_at":null,"days_open":28,"trust":{"level":"ok","repost_count":1,"flags":[],"days_open":28},"description":"Associate Director, Clinical AI Evaluation and Responsible Deployment\nAbout AISI\nAI Science & Innovation (AISI) sits at the centre of AstraZeneca’s R&D AI transformation within Enterprise AI Unit (EAI). Our remit is to build, buy, and deliver the AI models and agents that change pipeline outcomes across discovery, translational science, biomarkers, and clinical development, ultimately improve patients’ lives.\nWe are building an end-to-end Enterprise AI engine that unites data foundations, AI models, platforms, and business-facing applications to accelerate results across the value chain. Success comes from reusing what already works, sharing ideas across teams, and scaling impact rather than building in isolation.\nRole overview\nAstraZeneca is building an AI capability for Clinical Development that will improve how trials are designed, conducted, monitored, and analysed. We are hiring an Associate Director, Clinical AI Evaluation and Responsible Deployment to create the evidence systems that determine whether clinical AI is useful, reliable, safe, and ready to scale.\nThe role sits in the Applied Clinical AI team and engages directly with clinical stakeholders, Engineering, IT, and data teams to shape priorities, requirements, and delivery in collaboraiton with AstraZeneca’s existing Engineering, Product and Clinical Solutions teams.\nClinical AI rarely has simple ground truth. Expert judgments can differ, source data can be incomplete, and the consequence of an error depends on where an output appears in the workflow. You will turn these realities into rigorous evaluation environments, release criteria, monitoring strategies, and validation-ready evidence. You will work directly with clinical stakeholders to define what “good” means and directly with scientists and engineers to implement it in code.\nThis is a hands-on technical leadership role. You will build evaluation harnesses, analyse model and workflow behaviour, design experiments, and help teams diagnose failures-not merely review documents after development is complete. You will also connect scientific evaluation with Quality, Regulatory, and operational expectations so that evidence is useful both to builders and decision-makers.\n\nWhat you’ll do\nDefine and implement the evaluation strategy for clinical AI models and agents across development, release, monitoring, and change control. \nBuild evaluation harnesses, curated test sets, simulation environments, automated regression suites, and analysis pipelines in Python and related technologies. \nTranslate expert judgment from CRAs, medical monitors, clinical scientists, statisticians, and operations leaders into task definitions, scoring rubrics, error taxonomies, and clinically meaningful acceptance thresholds. \nEvaluate complete workflows-not only model outputs-including retrieval quality, tool selection, orchestration, source fidelity, abstention, human hand-offs, latency, and downstream operational impact. \nDesign approaches for noisy, sparse, delayed, or expert-dependent ground truth, including adjudication, inter-rater agreement, challenge sets, prospective studies, and post-deployment surveillance. \nLead failure analysis and red-teaming for hallucination, unsupported claims, automation bias, data leakage, prompt injection, subgroup performance, and unsafe workflow behaviour. \nEstablish traceability from intended use and user need through requirements, test evidence, and release decisions, including how model, prompt, tool, data, and workflow changes are assessed, monitored, and revalidated throughout the product lifecycle. \nWork with Quality, Regulatory, Clinical Operations, Privacy, Security, and R&D IT to align evaluation evidence with GCP, GxP, data-integrity, validation, and inspection-readiness expectations. \nAdvise teams on when evidence supports progression from prototype to controlled pilot, broader deployment, or regulated use-and when it does not. \nCreate reusable evaluation components and standards that can be adopted across agentic workflows for improving clinical operations and the wider Clinical Development AI portfolio. \nCommunicate findings and residual risks clearly to technical, clinical, quality, and executive audiences; ensure uncertainty is visible rather than hidden behind aggregate metrics. \nMentor scientists and engineers in rigorous experimentation, reproducibility, and responsible clinical AI development. \nEssential for the role\nPhD in Machine Learning, Computer Science, Statistics, Biostatistics, Biomedical Informatics, Computational Biology, or a related quantitative discipline; or an MD, master’s degree, or equivalent experience with a strong computational record. \n4 to 7 years of post-PhD (or equivalent) experience evaluating, validating, or deploying AI/ML systems in healthcare, life sciences, clinical research, or another safety- or evidence-critical environment. \nCurrent, hands-on coding ability in Python and SQL, including experience building automated evaluation pipelines, analysing large datasets, writing tests, and working in version-controlled environments. \nStrong grounding in experimental design, statistical inference, uncertainty, error analysis, and measurement reliability. \nExperience evaluating LLMs or agentic systems, including retrieval, tool use, structured outputs, multi-step workflows, robustness, and human-in-the-loop performance. \nDemonstrated ability to construct useful evaluation approaches when labels are noisy, expert opinions differ, or outcomes are delayed. \nExperience producing audit-ready evidence under GCP and GxP, including model-risk management, audit trails, and inspection readiness for regulated AI systems. \nAbility to work directly with clinical stakeholders to define intended use, harmful failure modes, decision thresholds, and acceptable human oversight. \nDeep understanding of trustworthy AI principles and lifecycle governance, and the ability to turn them into concrete release and monitoring criteria. \nExcellent communication skills and the judgment to explain technical evidence and residual risk to clinical, quality, regulatory, and executive audiences. \nExperience leading cross-functional technical work through influence, with the judgment to make evidence-based go or no-go recommendations under ambiguity. \nDesirable for the role\nDirect experience with clinical trial conduct, medical monitoring, pharmacovigilance, or clinical data management in a pharmaceutical, biotech, CRO, or health-system environment. \nExperience conducting prospective, silent-mode, shadow-mode, or human-factors evaluations in live clinical or healthcare workflows. \nFamiliarity with causal inference, calibration, subgroup analysis, weak supervision, active learning, or methods for learning with imperfect labels. \nExperience with LLM evaluation platforms, observability, MLOps/LLMOps, reproducible experimentation, and production monitoring. \nPeer-reviewed publications, standards work, open-source contributions, or regulator/industry-consortium engagement related to AI evaluation or medical AI. \nWhat success looks like\nEvery clinical AI release has clear intended use, measurable acceptance criteria, and traceable evidence. \nClinical experts recognize the evaluation as representative of real work and real failure modes. \nScientists and engineers can detect regressions quickly and diagnose why a system failed. \nQuality and regulatory partners are engaged early, with evidence generated by design rather than assembled after the fact. \nEvaluation assets are reused across products, studies, therapeutic areas, and deployment environments. \nOffice working requirements\nWhen we put unexpected teams in the same room, we unleash bold thinking with the power to inspire life-changing medicines. In-person working gives us the platform we need to connect, work at pace, and challenge perceptions. That’s why we work, on average, a minimum of three days per week from the office. We balance this expectation with individual flexibility. Join us in our unique and ambitious world.\nDate Posted\n29-oct-2026Closing Date\n12-nov-2026AstraZeneca embraces diversity and equality of opportunity. We are committed to building an inclusive and diverse team representing all backgrounds, with as wide a range of perspectives as possible, and harnessing industry-leading skills. We believe that the more inclusive we are, the better our work will be. We welcome and consider applications to join our team from all qualified candidates, regardless of their characteristics. 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